{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "2d145004-526a-4745-a163-a7f23f6f3baf"
    }
   },
   "outputs": [],
   "source": [
    "import pandas.io.data as web\n",
    "import datetime\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "faedb1a6-0512-4f85-9701-341f35e38af1"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "a77b0438-cdbc-4436-a618-589c6f4b0347"
    }
   },
   "outputs": [],
   "source": [
    "start = datetime.datetime(2016, 12, 1)\n",
    "end = datetime.datetime(2017, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "0f37596c-7e1f-4936-9f8c-d9c872bcda2a"
    }
   },
   "outputs": [],
   "source": [
    "f=web.DataReader(\"NVDA\", 'google', start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "f29674fc-4f71-4373-8fee-50c59bdd5346"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>92.10</td>\n",
       "      <td>92.17</td>\n",
       "      <td>84.77</td>\n",
       "      <td>87.64</td>\n",
       "      <td>25888074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-02</th>\n",
       "      <td>86.25</td>\n",
       "      <td>88.80</td>\n",
       "      <td>85.12</td>\n",
       "      <td>88.45</td>\n",
       "      <td>11957300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-05</th>\n",
       "      <td>89.99</td>\n",
       "      <td>92.61</td>\n",
       "      <td>89.00</td>\n",
       "      <td>91.88</td>\n",
       "      <td>15057125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-06</th>\n",
       "      <td>92.21</td>\n",
       "      <td>93.73</td>\n",
       "      <td>91.57</td>\n",
       "      <td>93.39</td>\n",
       "      <td>12469875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-07</th>\n",
       "      <td>92.84</td>\n",
       "      <td>95.30</td>\n",
       "      <td>92.10</td>\n",
       "      <td>95.07</td>\n",
       "      <td>12020093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume\n",
       "Date                                            \n",
       "2016-12-01  92.10  92.17  84.77  87.64  25888074\n",
       "2016-12-02  86.25  88.80  85.12  88.45  11957300\n",
       "2016-12-05  89.99  92.61  89.00  91.88  15057125\n",
       "2016-12-06  92.21  93.73  91.57  93.39  12469875\n",
       "2016-12-07  92.84  95.30  92.10  95.07  12020093"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "96f850bc-0d7e-4a72-a076-f94040d72410"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0ae8d7828>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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nTqZG/wtZ124/zG47t+y4SEXLlIKyU5xzjxT67Uxr7QJgvLW2hXPu+629xlrb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\nM96OfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0ae882128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f.plot(y=\"Close\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "f35e16fb-1cdc-423d-b840-fd386e00d9f7"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0b03d4c50>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LaqfW7Velg+ya/RS4A3hB02XfAp4PjLU76m6Rbbt2y2rbtVtWO7UuUN0BPw1Y\n13LZkUC33Y50i2zbtVtW267dstqpdQFfg5ckqZYquQUfQtiegUcWrrbbuW6Rbbt2y2rbtVtWO7Vu\nv0ptwYcQTgDeC+zVctV/A5+JMX7Dbv5ukW27dstq27VbVju1bqvKHEUfQvgA8DngUrLXIPZp/DoS\nWAx8LoTwfrv5uimu2W6a3RTXbDfNboprLvK+aFWlXfSnAvNijLHl8tuBn4YQfgucC5xnN1c3xTXb\nTbOb4prtptlNcc1F3hcDVGYLHtiR7CP0hnML8Cy7ubtFtu3aLatt125Z7dS6g1RpwN8EfDiEMGiv\nQghhLPChxm3s5usW2bZrt6y2XbtltVPrDlK1XfRXAQ+HEK5l4CfsHAqsBY6ym7ub4prtptlNcc12\n0+ymuOYi74sBqnYU/QTgH4BZNL11APglcHGM8VG7+bsprtlumt0U12w3zW6Kay7yvmhWqQEvSZI6\no0q76AFovC4xg4E/1dwWY2w9nZ/dirXt2i2rbdduWe3Uus0qswUfQhgDfAL4Z2D7lqtXA18EemKM\n6+22301xzXbT7Ka4ZrtpdlNcc5H3RasqbcF/Gjge+DDZAQjNBx4cBXwS2JrsCEO77XdTXLPdNLsp\nrtlumt0U11zkfTFAlQb8ccA/xhivarn8XuDCEMIfgIW0/4e2W3zbrt2y2nbtltVOrTtIld4HPwF4\ncCPXPwRsZzd3t8i2Xbtlte3aLaudWneQKg34nwLnhRAGncGncdnZjdvYzdctsm3Xblltu3bLaqfW\nHaRKu+hPAq4AHgoh3MLA1yVeCNwGvM5u7m6Ka7abZjfFNdtNs5vimou8LwaozFH0sOHowqMZ+s3/\nP8x7VKHddNdsN81uimu2m2Y3xTUXeV80q9SAlyRJnVGl1+AlSVKHVHLAhxDuCSH8qOWyH4cQ7rY7\n+m6Rbbt2y2rbtVtWO7VuvyodZNfsImBFy2WXMPrPyLVbfNuu3bLadu2W1U6tC/gavCRJtVTJXfSS\nJGl0KrWLvvEm/7cDL2HgWwd+AXwzxti6K8NuRdp27aa+ZrtpdlNcc5H3RbPK7KIPIRxIduL9J4Af\nM/DN/0cC44GjY4y/stt+N8U1202zm+Ka7abZTXHNRd4Xraq0Bf8F4DvASTHGAT91hBC6gK82bvMS\nu7m6Ka7ZbprdFNdsN81uimsu8r4YoEqvwb8IOL/1DwzQuOx84MV2c3eLbNu1W1bbrt2y2ql1B6nS\ngH8YOGhkjP8OAAAV3UlEQVQj1x/E07sy7OaT2prtptktsm3Xblnt1LqDVGkX/Xlkn4U7E7iawa9L\nnAi8327uboprtptmN8U1202zm+Kai7wvBqjMQXYAIYS/A04DZgJjGxf3AsuAz8YYo9383RTXbDfN\nboprtptmN8U1F3lfNKvUgO8XQujm6TP5PBJjXGe3c90i23btltW2a7esdmrdfpUc8JIkaXSq9Bo8\nIYQXAKcy+M3/vwS+GGO8zW7+boprtptmN8U1202zm+Kai7wvmlVmCz6EMBtYDNxMdhKA5gMPXkX2\nWsUbYoxX2W2/m+Ka7abZTXHNdtPsprjmIu+LVlXagv80cHaM8WNDXPfxEMLHgXPJ7hC77XeLbNu1\nW1bbrt2y2ql1B6nS++CfD/y/jVy/CHie3dzdItt27ZbVtmu3rHZq3UGqNODvBV67ketfC/zBbu5u\nkW27dstq27VbVju17iBV2kX/MeDiEMIrGPoE/K8G3mo3dzfFNdtNs5vimu2m2U1xzUXeFwNU5iA7\ngBDCS4F3M/SRhZ+LMf7Sbv5uimu2m2Y3xTXbTbOb4pqLvC+aVWrAS5KkzqjSa/CSJKlDHPCSJNWQ\nA16SpBpywEuSVEMOeEmSaqhK74PfqBDCTsA7Y4yfyPn1uwKrYoz/03J5N/CSGOO1o1xfF/AKYDrw\nEHBVno/+a6zzbzHGRxq/PwQ4Cdid7OQHXxrFWzPeB/xnjLEjJ1Foab8OOIjsz319COEI4P1kP0R+\nL8Z4Yc7utsBc4OXAzsB64G5gcYzx6lGuueOPiRDCDsC+wG9jjCtDCM8C/gkYB3wnxnj7aNbc8r3u\nBo6OMd7ZoV5HHsONVpGP40IeEyGErYE3MvitS78ALo0xrs3b3sj3rNzzWhGP4SKfexr9pJ5/ynqs\nJfM2uRDCi4CbY4xj2/y6nYFLyU7g3wdcDJzS/w+i8Q/swRzdK4C5McbVIYTJwBVkD7BHgB2A3wGH\nxhhXtNm9AfhkjPH7IYQ3AN8Dvg/cTnaKw9cBx8YYv99Ot9FeT/YAvQb4OnBJJx5IIYR3Al8Efkt2\nisV/Br4M/AfQCxwHfCTG+Lk2u9PJTgSxLbAG2JXsfn4WcADZffPWGONTbXaLekwcBPwQmAisIvvg\niO8AT5E90TwHeHmM8eY2u+8e5qrPAueQPTEQY/x8m91CHsONdiGP4wIfE9PJzv39HOAGBp585GDg\nj8DsGONd7XRH8H2r9rxW1GO4kOeeRju155/SHmuV2YIPIey7iZvslTP9abIH1sHApMbvrwkhHBVj\n/GvjNl05uq8m+4kW4P8CE4A9Y4z3NH6qXgx8Aji5ze4MYHnj/z8CfDTGeHb/lSGEUxvdtgd8wwlk\nPzl+C3g0hPDvwNdjjLfm7EF2woaTY4xfDyEcTvaP4H0xxi831rwU+CDQ1j8w4PPAlY12XwjhQ8Bh\nMcZZIYTnkT0RnQ58vM1uUY+JT5E9Gb4XeCfZY+DKGOOJACGEfwPOAOa02b0AeIDsSbbZGLInr3Vk\nT/JtDXiKewxDcY/joh4TXwFuAfaLMT7afEUIYSKwEPgScHQ70QSf14p6DEMxzz2Q3vNPIY+1oVTp\nNfjfAL9u/Lf116+Bb+fsvhJ4d4zxVzHGHwMvI9v9+JPGVgtkT46jcQTZT4j3AMQY/wh8iHx/QU+R\nPdECTAOWtFy/hPxPCgBXxBjfSPbT6Dlka/xtCOHGEMKJIYQJG//yIU0je7ATY7wGGAs07xr8KfDc\nHN3DgM/EGPv/fs4HXhlC2KGxW/r/AG/L0S3qMTET+GyM8TGyJ5PnAF9ruv6LwIE5uheSbVW/JsY4\nrf8X2dbJUY3f75Gj26yTj2Eo7nFc1GPiZcDprU+4AI3LzgAOydFN7XmtqMcwFPPcA+k9/xT1WBuk\nSgN+JXAi2V9W6689yHbp5bE90P8TLTHGNcCxZCf8vwbYMfeKn/4H9Ezg9y3X3UX2j6NdPyN7zQey\nJ4BXtFx/ONnW3KjEGP8cYzwnxrh343vcRvYAfihH7i80/gGFEJ5Dtmdo96brn0v299uuVTw9JADG\nN9r9u/b+i+x1sXYV9ZjYGniy0VwHPEE2mPv17/puS4zxJLKt3asaW76dVMRjGIp7HBf1mFgFTN3I\n9VMbt2lXas9rhTyGm3X4uQfSe/4p6rE2SGV20QPLgOcMdxBGCGES+XY53U12wMiGA5FijE+FEN5M\ntisq765ugG+GENYA3WT/YJc3Xfds8v0lfRj4eeOBeh3wqRDCgWSvXe4F/B3ZwUp5DPkTfYzx543v\n+e5Gv12XAt8IIVwEHEO2i+kzIYQ+sq3M82j8hN2mHwGfDSGcRPYa2FnAbxpbF5D9I/5zjm5Rj4n7\nyZ607238/i0MfNLamYFPliMWY7wkhHAjsDCE8FpgXs41tiriMQzFPY6Lekx8ney+/SRwNYM/AOR0\n4As5uqk9rxX1GC7quQfSe/4p6rE2SJW24L/K0w+qodxHvie1JcA7Wi9sHBjxZrJdZXlcRPaXu5rs\nATa+5fo35Wk3jlA9mOwn6Q8C2wF/T/Y6z3TgLTHGb+Zc80afSGKMj8YYv7ax2wzjQ2S7wd5C9md+\nB/ANGq/fkf2E/ZEc3Q+SvUZ8G9nW5Cyyo3n7TQHOzdEt6jHxbZq2nGKMP4gxPtl0/THAjTnbxBgf\nINs1ey3ZVnGewdCskMcwFPo4LuQxEWP8GHA28AGyP/ODjV+/aVx2dozx4znWm9rzWlGP4aKeeyCx\n558CH2uDJHMUfV4hhK2A8UO93tF0/S7D/YQ9iu+7HdAbY/zbKBpdZP/YxgCPxJxvWdpcQgjbAN1N\nP/Hm7TyP7B/aHe0esTpMb3M9JsaTPSbWdKA1k+ytOwubDqrqqE48hhudjj+OO/2YaGlPo+mtS/3H\nJVRJHR7DRavq809Lu9DHWu0HvCRJW6IqvQZP44QKb2foN/9/M+Z4P27Nur8EFuTtprjmota7ie+5\nGzA/xvh2u53vFtkeTbdxUpOZwMoY420t120DhBjjQrvV6qa45hDC3mS7/H8ZY7wjhPC/gPeQ7Sn4\n9xjjT9ptDqUyr8E3DsD5Hdl7GleTvc54beP/3w3cEUI4YAvvvitvN8U1F7XeEZhMvre/2N387Vzd\nEMLzyQ4AvBa4JYTws5CdTKbf9sACu9XqprjmEMKryV5vPw/4deP315Idm/Jc4IchOxPfqFVpC/4L\nZEd/ntT0vkNgw2t4X23c5iV2c3VTXHMh3RDCMZu4Sa73lNstvl3gms8GbiU7Q9kkspMLXR9CeEWM\n8b6cTbvFd1Nc88eAc2OMp4cQ3kJ2FsKvxBj/BSCEcBbZu1BGvRVfpQH/IuD41idygJidReh8siOH\n7ebrFtlOrbuY7G07GzuyN8/BKXaLbxfVfSnwypidO/+REMLryU53+vOQnR3t8RxNu8V3U1zzDLIz\nUAJEsjP7/WfT9f+PDr0NtkoD/mGy82DfMcz1B/H0+wXt5pPamovqPkR23u5Lh7oyhPBisvcv283X\nLbJdVHdbmk4F3Pih8uQQwhfJTtrz1hxNu8V3i2wXuea+RnN9COFvZC879nuMbPf/qFVpwJ8HXBiy\nt/8M9eb/E8k+Hchuvm6Kay6qu4zswJkhhwSb3kK0u/naRXXvINsVO+CT0mKMp4YQAC7L0bRbfLfI\ndlHde8k+FKf/zJEvITsfQr/dyX9WvwEqc5BdjPFLZAfHHAx8l+zo6182/v9gsl21X7abr5vimgu8\nL84lOwp/OHeRnUrVbr5uke2iupfw9Kl1B4gxngosIt8PDnaL7RbZLqr7FbLz5fe3bm15f/1sOvD6\nO1T0ffAh+yzjZzV+27ETvNgtvp1aV5LqqjJb8M1ijOtijA+RfRDB1nY72y2ynVq3XwhhbsjO3Ga3\ngG6Rbbt2y2qn1q3kgG/yr2Svt9otpltk267dstp27ZbVTqpb9QE/2g/TsLv52nbtltW2a7esdlLd\nqg94SZKUQ9UH/GzgAbuFdYts27VbVtuu3bLaSXUreRS9JEkanSqd6IYQwouA1wMrgdg4RWD/dROB\nC3J+SpTdRNdsN81uimu2m2Y3xTUXeV80q8wu+hDCUcCNwFuAD5F9UljzCSu2Jd+nRNlNdM120+ym\nuGa7aXZTXHOR90Wrygx44OPAeTHGfYCpwDnAZSH7KD27o+8W2bZrt6y2XbtltVPrDlKlXfQzgH+E\nDSf1PyeE8EfgP0P2kXo32R1VN8U1202zm+Ka7abZTXHNRd4XA1RpC34N2WfubhBjvBg4AfgPYI7d\nUXWLbNu1W1bbrt2y2ql1B6nSgP8NQ3xIRIzx22R/8M/bHVW3yLZdu2W17dotq51ad5AqDfivALsM\ndUWMcRFwPHCt3dzdItt27ZbVtmu3rHZq3UF8H7wkSTVUpS14SZLUIckM+BDCi0IIvXaL6RbZtmu3\nrLZdu2W1U+gmM+AbkvoknwS7Rbbt2i2rbdduWe1KdyvzPvgQwvc2cZPtgbYPGLBbfNuu3bLadu2W\n1U6tO5TKDHiy8/L+CPjTMNePtTuqbpFtu3bLatu1W1Y7te4gVRrwtwPfjTF+Y6grQwgvBl5nN3e3\nyLZdu2W17dotq51ad5AqvQa/DNh/I9evAe6zm7tbZNuu3bLadu2W1U6tO0iVtuBPYiO7JmKMtwPT\n7ObuFtm2a7estl27ZbVT6w7iiW4kSaqhKu2iHySE8IMQws52i+kW2bZrt6y2XbtltVPrVnrAA4cC\n29otrFtk267dstp27ZbVTqpb9QEvSZJyqPqA/wOwzm5h3SLbdu2W1bZrt6x2Ul0PspMkqYaq9Da5\nAUIIk4A3A7uT/XTznRjjarud6RbZtmu3rLZdu2W1U+tChbbgG+fnvTjG+J8hhBnAT8nOx3s3MLXx\n/0c03iNot81uimu2m2Y3xTXbTbOb4pqLvC9aVek1+FcAtzb+/1zgh8CuMcZZwG7AD4AL7Obuprhm\nu2l2U1yz3TS7Ka65qO4gVRrw2/D0QQYvBs6LMa4FiDGuA84BDrabu5vimu2m2U1xzXbT7Ka45iLv\niwGqNOD/Czii8f8PA89tuf65wJN2c3eLbNu1W1bbrt2y2ql1B6nSQXafBBaGENYBnwfODyHsQPbJ\nO3sB84Fv2c3dTXHNdtPsprhmu2l2U1xzkffFAJU5yA4ghPAmstcengN0NV21Bvgq8P4YY6/dfN0U\n12w3zW6Ka7abZjfFNRd5XzSr1IAHCCGMJfsovT3IXkJ4CFgWY3zM7ui7Rbbt2i2rbdduWe3Uus0q\nN+AlSdLoVekgu40KITwzhHCc3WK6Rbbt2i2rbdduWe0UuskMeLKz/CywW1i3yLZdu2W17dotq135\nbmWOog8hTNzETSbYzd8tsm3Xblltu3bLaqfWHUplBjywiuwUfcPp2sT1djdf267dstp27ZbVTq07\nSJUG/GPAp4Abhrn+ecC/2s3dLbJt125Zbbt2y2qn1h2kSgP+ZoAY48+GujKEsIqB7xe0W522Xbtl\nte3aLaudWneQKh1kdzHwt41c/zDZGX7s5usW2bZrt6y2XbtltVPrDuL74CVJqqEqbcFLkqQOqdJr\n8IQQtgbeCLwEeHbj4oeBXwCX9n+knt183RTXbDfNboprtptmN8U1F3lfNKvMLvoQwnTgKrKT798A\n/Klx1U5kn437R2B2jPEuu+13U1yz3TS7Ka7ZbprdFNdc5H3Rqkpb8F8BbgH2izE+2nxF48QAC4Ev\nAUfbzdVNcc120+ymuGa7aXZTXHOR98UAVXoN/mXA6a1/YIDGZWcAh9jN3S2ybdduWW27dstqp9Yd\npEoDfhUwdSPXT23cxm6+bpFtu3bLatu1W1Y7te4gVdpF/3VgYQjhk8DVDHxd4kjgdOALdnN3U1yz\n3TS7Ka7ZbprdFNdc5H0xQGUOsgMIIXwIeA/ZUYX9C+siO7rwghjjOXbzd1Ncs900uymu2W6a3RTX\nXOR90axSA75fCGEaTW8diDHeY7dz3SLbdu2W1bZrt6x2at1+lRzwkiRpdKr0GjwhhG2BmcDKGONt\nLddtA4QY40K7+boprtlumt0U12w3zW6Kay7yvmhWmaPoQwjPB24HrgVuCSH8LISwc9NNtgcW2M3X\nTXHNdtPsprhmu2l2U1xzkfdFq8oMeOBs4FZgR2Avss/MvT6EsLvdjnSLbNu1W1bbrt2y2ql1B6nS\ngH8p8JEY4yMxO0Xf68lO5/fzEMIedkfdTXHNdtPsprhmu2l2U1xzkffFAFUa8NsCT/X/JsbYF2M8\nGbgc+BnwfLuj6hbZtmu3rLZdu2W1U+sOUqUBfwdwQOuFMcZTgUuBy+yOqltk267dstp27ZbVTq07\nSJUG/CXA3KGuaPzBF5GdCMBuvm6Rbbt2y2rbtVtWO7XuIL4PXpKkGqrSFrwkSeoQB7wkSTXkgJck\nqYYc8JIk1ZADXpKkGnLAS5JUQw54SZJqqFIfFytpdEIIb2PgJ1GtAVYCtwA/ABbEGP8nR/clwFHA\n+THGRzuxVknFcgteqp8+4HTgH4CTgM83LruA7OMpX5ij+VLgY8CkTi1SUrHcgpfq6coY481Nvz87\nhPAKsq34S0MIe8cY17TR68ipMyWVxwEvbSFijD8NIXwS+BTZ1v03Glvz7wUOBZ4DrAKuAD4QY1wJ\nEELoAXrI9gLcG0Kg8f/TYoz3NW7zD8D/AV4APAn8sNH4Y3l/QknN3EUvbVm+RbY1flTj968CpgH/\nBvR/0MVbyLb0+323cTnAe8h+OPhHYAVACOFfgIuA/wZOA84HjgR+FkKYWOCfRdJGuAUvbUFijA+E\nEFYDezYu+lKM8bPNtwkh3ABcHEJ4WYzx+hjjrSGEm8kG/6X9W+2N2+4OfBz4aIzx7KbLvwf8BjgF\n+HShfyhJQ3LAS1ue/wEmADS/Dh9CGAc8A7iBbCt/f+D6TbTe1Ljtd0IIOzRd/mfgTuBwHPDSZuGA\nl7Y8zwD+BBBCeCbZFvjfATs23aYP2H4ErelkL/XdNcR1fcDa0SxUUn4OeGkLEkLYhWxw39m46DvA\nLOAc4LdkW/djgKsY2TE6Y4D1wKsb/23V9nvuJXWGA17ashxHtmV9VQhhEnAEcEaM8VP9NwghTB/i\n6/qG6f2ebBf9vTHGobbiJW0mHkUvbSFCCEeQnQDnbuBioLdxVevzwGkMHuiPN/7beqKb75FtufcM\n8z0n512vpNFxC16qny7gNSGEvcn+je9EtqX+KuAe4JgY41pgbQjhWuCDIYStgQfI3j43lcEntlnW\nuOzMEMK3gXXAZTHGu0MIpzcunwYsBh4D9gDeCPwr8Fkklc4teKl++oD5wELgq2TvXQd4N/CiGOPt\nTbedS/Z6+ynAmWTnrp/daGzYio8x/ops639fsnPdXwxMaVx3NtnR9L1kp7M9F3gdcCVwWRF/QEmb\n1tXXN9xLa5IkKVVuwUuSVEMOeEmSasgBL0lSDTngJUmqIQe8JEk15ICXJKmGHPCSJNWQA16SpBpy\nwEuSVEMOeEmSasgBL0lSDTngJUmqof8P5dwMo9dSI0sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0ae519a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "f.plot.bar(y=\"Volume\", color=\"Blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
